arXiv:2607. 28047v1 Announce Type: cross Abstract: Time-varying implicit neural representations (INRs) provide a compact representation of scientific volumes and, for modalities such as dynamic X-ray computed tomography (CT), are often the only practical way to represent the data.
By Alper Sahistan, Haichao Miao, Zhimin Li, Peer-Timo Bremer, Joshua A Levine, Valerio Pascucci
Time-varying implicit neural representations (INRs) provide a compact representation of scientific volumes and, for modalities such as dynamic X-ray computed tomography (CT), are often the only practical way to represent the data. However, interactive volume rendering of INRs is challenging, as cheap memory lookups are replaced by expensive neural inferences, hindering the performance.
arXiv:2512.06174v3 Announce Type: replace
Abstract: Generating realistic cast shadows for inserted foreground objects requires reasoning about scene geometry and illumination. However, most learning-...
By Shilin Hu, Jingyi Xu, Akshat Dave, Dimitris Samaras, Hieu Le
WildShadowRemover is a framework that adapts a pretrained video diffusion model for robust in-the-wild video shadow removal using LoRA fine-tuning. It augments the frozen VAE decoder with a detail injection module and introduces a shadow‑mask‑guided frequency‑decomposed modulation module to restore high‑frequency textures while suppressing shadow artifacts, with monocular depth priors providing geometry‑aware guidance. The authors also create WildShadow, a large‑scale paired video shadow removal dataset, and show that their method outperforms existing approaches in shadow removal quality, temporal consistency, and generalization across challenging real‑world scenarios.
By Jiamin Xu, Cong Wang, Zheng Dong, Chi Wang, Renshu Gu, Weiwei Xu, Gang Xu
PureLight introduces a neural approach to estimate the appearance of complex luminaires that are difficult for traditional path tracing, such as small emitters surrounded by multiple specular layers. The method uses light tracing to build paths from emitters to exit surfaces and learns the probability density function of outgoing radiance with a large normalizing flow network, then distills this into a lightweight MLP for efficient inference. Additionally, a sampling network and a blending network are trained to compute direct illumination and composite the luminaire into arbitrary scenes, enabling low‑sample rendering of challenging luminaires.
By Pedro Figueiredo, Zixuan Li, Beibei Wang, Milo\v{s} Ha\v{s}an, Nima Khademi Kalantari
Existing supervised and unsupervised shadow removal methods often suffer from limited generalization due to the insufficient diversity of available training datasets, while zero-shot methods tend to produce artifacts and require time-consuming test-time optimization. To address these issues, we propose FreeShadow, a training-free shadow removal method built upon pretrained diffusion models, which exploits diffusion priors for shadow removal without any training or optimization.